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Cindy L Christiansen

Publications and source records attributed to Cindy L Christiansen.

7 recordsLinked to original sources
PubMed2003

Tooth retention as an indicator of quality dental care: development of a risk adjustment model.

BACKGROUND: Retaining teeth improves oral health and quality of life. Thus, receipt of a root canal (vs. a tooth extraction) is a useful indicator of the quality of dental care. However, use of this quality measure without adjusting for the severity of oral disease could lead to spurious conclusions. OBJECTIVES: This paper describes the development of a dental severity adjustment methodology. RESEARCH DESIGN: Retrospective study. SUBJECTS: 54,423 users of Department of Veterans Affairs (VA) dental care who had either root canal therapy or a tooth extraction at a VA facility in Fiscal year 1998. MEASURES: International Classification of Disease Clinical Modification codes for dental diagnoses and comorbid medical conditions. We modeled the effects of dental disease severity in logistic regression models of the probability of receiving a root canal, using both conceptual and Modified Delphi-Panel derived models, adjusting for age, and medical comorbidities. RESULTS: Conceptual and Modified Delphi models performed similarly. The dental disease severity adjustments increased the fit in models of the probability of receiving a root canal (C-statistic = 0.822 for the conceptual model and 0.804 for the Modified Delphi Panel model) compared with the model including comorbid medical conditions alone (C-statistic = 0.561). CONCLUSIONS: Risk adjustment for dental disease severity improves the fit of models of the probability of receiving a root canal. Studies of the quality of dental care should consider employing risk-adjusted models.

Alcoholism↗
PubMed2002

Screening mammograms by community radiologists: variability in false-positive rates.

BACKGROUND: Previous studies have shown that the agreement among radiologists interpreting a test set of mammograms is relatively low. However, data available from real-world settings are sparse. We studied mammographic examination interpretations by radiologists practicing in a community setting and evaluated whether the variability in false-positive rates could be explained by patient, radiologist, and/or testing characteristics. METHODS: We used medical records on randomly selected women aged 40-69 years who had had at least one screening mammographic examination in a community setting between January 1, 1985, and June 30, 1993. Twenty-four radiologists interpreted 8734 screening mammograms from 2169 women. Hierarchical logistic regression models were used to examine the impact of patient, radiologist, and testing characteristics. All statistical tests were two-sided. RESULTS: Radiologists varied widely in mammographic examination interpretations, with a mass noted in 0%-7.9%, calcification in 0%-21.3%, and fibrocystic changes in 1.6%-27.8% of mammograms read. False-positive rates ranged from 2.6% to 15.9%. Younger and more recently trained radiologists had higher false-positive rates. Adjustment for patient, radiologist, and testing characteristics narrowed the range of false-positive rates to 3.5%-7.9%. If a woman went to two randomly selected radiologists, her odds, after adjustment, of having a false-positive reading would be 1.5 times greater for the radiologist at higher risk of a false-positive reading, compared with the radiologist at lowest risk (95% highest posterior density interval [similar to a confidence interval] = 1.17 to 2.08). CONCLUSION: Community radiologists varied widely in their false-positive rates in screening mammograms; this variability range was reduced by half, but not eliminated, after statistical adjustment for patient, radiologist, and testing characteristics. These characteristics need to be considered when evaluating false-positive rates in community mammographic examination screening.

Adult↗
PubMed2002

Effectiveness of thrombolytic therapy for acute myocardial infarction in the elderly: cause for concern in the old-old.

BACKGROUND: National guidelines have encouraged increased use of thrombolytic therapy for elderly patients with acute myocardial infarction (AMI). However, evidence supporting thrombolytic therapy in patients 75 years and older is lacking. In a retrospective cohort study of 2659 elderly AMI patients, we determined the association between thrombolytic use and in-hospital mortality by age and among patients with or without absolute or relative contraindications to thrombolytic treatment. METHODS: We abstracted the medical records of 2659 elderly patients admitted with AMI at 37 Minnesota community hospitals between 1992 and 1996. The main outcome measure was in-hospital mortality, controlling for demographic, clinical, comorbidity, and severity-of-illness variables. RESULTS: Sixty-three percent of 719 eligible patients received thrombolytic therapy. Twenty-seven percent of thrombolytic recipients had absolute contraindications to treatment. Patients receiving thrombolytic agents had fewer and less severe comorbidities than those not receiving thrombolytic therapy. There was a 4% increase in the odds of death for every 1-year increase in age for all thrombolytic recipients vs nonrecipients (odds ratio [OR], 1.04 per year; 95% confidence interval [CI], 1.01-1.08; P =.03). Among patients with 1 or more contraindication, the OR for death associated with thrombolytic use was 1.57 (95% CI, 1.03-2.40; P =.04). The adjusted odds of death among eligible thrombolytic recipients (vs nonrecipients) increased significantly with age (OR, 1.08 per year; 95% CI, 1.02-1.14; P =.008). Among eligible patients aged 80 to 90 years, the predicted odds of death among thrombolytic recipients vs nonrecipients was 1.4. Among eligible patients younger than 80 years, thrombolytic use was associated with reduced mortality. CONCLUSIONS: Our findings suggest the need for more research on the effectiveness of thrombolytic therapy for AMI patients 75 years and older and for more careful selection of elderly patients for this treatment.

Aged↗
PubMed2002

Profiling nursing homes using Bayesian hierarchical modeling.

OBJECTIVES: New methods developed to improve the statistical basis of provider profiling may be particularly applicable to nursing homes. We examine the use of Bayesian hierarchical modeling in profiling nursing homes on their rate of pressure ulcer development. DESIGN: Observational study using Minimum Data Set data from 1997 and 1998. SETTING: A for-profit nursing home chain. PARTICIPANTS: Residents of 108 nursing homes who were without a pressure ulcer on an index assessment. MEASUREMENTS: Nursing homes were compared on their performance on risk-adjusted rates of pressure ulcer development calculated using standard statistical techniques and Bayesian hierarchical modeling. RESULTS: Bayesian estimates of nursing home performance differed considerably from rates calculated using standard statistical techniques. The range of risk-adjusted rates among nursing homes was 0% to 14.3% using standard methods and 1.0% to 4.8% using Bayesian analysis. Fifteen nursing homes were designated as outliers based on their z scores, and two were outliers using Bayesian modeling. Only one nursing home had greater than a 50% probability of having a true rate of ulcer development exceeding 4%. CONCLUSIONS: Bayesian hierarchical modeling can be successfully applied to the problem of profiling nursing homes. Results obtained from Bayesian modeling are different from those obtained using standard statistical techniques. The continued evaluation and application of this new methodology in nursing homes may ensure that consumers and providers have the most accurate information regarding performance.

Bayes Theorem↗
PubMed2002

Risk-adjusted mortality rates as a potential outcome indicator for outpatient quality assessments.

OBJECTIVE: The quality of outpatient medical care is increasingly recognized as having an important impact on mortality. We examined whether a clinically credible risk adjustment methodology can be developed for outpatient quality assessments. RESEARCH DESIGN: This study used data from the 1998 National Survey of Ambulatory Care Patients, a prospective monitoring system of outcomes of patients receiving ambulatory care in the Veterans Affairs (VA) integrated service networks. SUBJECTS: Thirty-one thousand eight hundred twenty-three patients were followed for 18 months. MEASURES: The main study outcome measures were observed and risk-adjusted mortality rates. RESULTS: Of the 31,823 patients, 1559 (5%) died during the 18-months of follow-up. Observed mortality rates across the 22 VA integrated service networks varied significantly from 3.3% to 6.7% (P <0.001). Age, gender, comorbidities (Charlson Index), physical health, and mental health were significant predictors of dying. The resulting risk-adjusted mortality model performed well in cross-validated tests of discrimination (c-statistic = 0.768; 95% CI, 0.749-0.788) and calibration. Analysis of variance confirmed that the 22 integrated service networks differed in their average level of expected risk (P <0.001). Risk-adjusted rates and ranks of the networks differed considerably from unadjusted ratings. CONCLUSIONS: Risk-adjusted mortality rates may be a useful outcome measure for assessing quality of outpatient care. We have developed a clinically credible risk adjustment model with good performance properties using sociodemographics, diagnoses, and functional status data. The resulting risk adjustment model altered assessments of the performance of the integrated service networks when compared with the unadjusted mortality rates.

Aged↗
PubMedDate not supplied

Do different case-mix measures affect assessments of provider efficiency? Lessons from the Department of Veterans Affairs.

Although case-mix adjustment is critical for provider profiling, little is known regarding whether different case-mix measures affect assessments of provider efficiency. We examine whether two case-mix measures, Adjusted Clinical Groups (ACGs) and Diagnostic Cost Groups (DCGs), result in different assessments of efficiency across service networks within the Department of Veterans Affairs (VA). Three profiling indicators examine variation in resource use. Although results from the ACGs and DCGs generally agree on which networks have greater or lesser efficiency than average, assessments of individual network efficiency vary depending upon the case-mix measure used. This suggests that caution should be used so that providers are not misclassified based on reported efficiency.

Aged↗